{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x0b'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import struct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "b11 = struct.pack('>B', 11) # 只占了一个字节\n",
    "b11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11,)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d11 = struct.unpack('>B', b11)\n",
    "d11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x00\\x00\\x00\\x0b'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b11 = struct.pack('>i', 11) # 占了四个字节\n",
    "b11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d11 = struct.unpack('>i', b11)\n",
    "d11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\\x00\\x00\\x08\\x03\\x00\\x00\\xea`\\x00\\x00\\x00\\x1c\\x00\\x00\\x00\\x1c' \n",
      " (2051, 60000, 28, 28) \n",
      " 47040000 \n",
      " <class 'bytes'> \n",
      " <class 'tuple'> 47040000\n"
     ]
    }
   ],
   "source": [
    "with open('./MNIST_data/train-images-idx3-ubyte', 'rb') as f:\n",
    "    buffer = f.read(4*4)\n",
    "    head = struct.unpack('>iiii', buffer)\n",
    "    length = head[1]*head[2]*head[3]\n",
    "    buffer_r = f.read(length)\n",
    "    data = struct.unpack('>{}B'.format(length), buffer_r)\n",
    "    \n",
    "print(buffer,'\\n', head, '\\n', length, '\\n', type(buffer_r), '\\n', type(data), len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#img1 = np.reshape(data[784*0:(784*0)+(28*28)], (28,28))\n",
    "def show_img(i=0):\n",
    "    img1 = np.reshape(data[784*i:784*(i+1)], (28,28))\n",
    "    plt.imshow(img1, cmap='gray')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F+IGgCD8QFOEHgiL8QFCEHwiK8ANB/R/7QknxGq+fLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pZulXVeJ7V+SM14X0wxl+QEyc4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/ArhnQxpb7eLPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADXNJREFUeJzt3X+oVHUax/HPs5VFaZiFdildXautMNLtFkW1tInhLoYF/ZL+cNllr39UbCG4UZDCGtSSbitRYGgZlBWYm8SyGSFrwhJaSZlWmtzspujG7Yf1j6XP/nGPcbM73zN35pw5c+/zfoHMzHnmnPMw9bnnzJwfX3N3AYjnZ1U3AKAahB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFDHt3JlZsbphEDJ3N3qeV9TW34zm2lmH5rZLjO7t5llAWgta/TcfjM7TtJHkmZI6pG0WdIcd9+emIctP1CyVmz5L5O0y913u/shSc9Lmt3E8gC0UDPhP0vSp/1e92TTfsTMusxsi5ltaWJdAArWzA9+A+1a/GS33t2XS1ousdsPtJNmtvw9ksb3e322pL3NtQOgVZoJ/2ZJ55rZJDMbIek2SeuKaQtA2Rre7Xf3783sTkmvSjpO0kp3f7+wzgCUquFDfQ2tjO/8QOlacpIPgKGL8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgWjpEN8px4YUX1qzNmjUrOW9XV1eyvnnz5mT9nXfeSdZTHn300WT90KFDDS8b+djyA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQTY3Sa2bdkg5KOizpe3fvzHk/o/Q2YN68ecn6I488UrM2cuTIotspzLXXXpusb9iwoUWdDC/1jtJbxEk+v3H3zwtYDoAWYrcfCKrZ8Luk9Wb2lpmlzxMF0Faa3e2/0t33mtlYSa+Z2QfuvrH/G7I/CvxhANpMU1t+d9+bPR6QtFbSZQO8Z7m7d+b9GAigtRoOv5mdYmajjj6XdJ2kbUU1BqBczez2j5O01syOLuc5d/93IV0BKF1Tx/kHvTKO8zdkzJgxyfqOHTtq1saOHVt0O4X58ssvk/Vbb701WV+/fn2R7Qwb9R7n51AfEBThB4Ii/EBQhB8IivADQRF+IChu3T0E9Pb2JusLFy6sWVuyZEly3pNPPjlZ37NnT7I+YcKEZD1l9OjRyfrMmTOTdQ71NYctPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ExSW9w9zWrVuT9YsvvjhZ37YtfX+WKVOmDLqnek2ePDlZ3717d2nrHsq4pBdAEuEHgiL8QFCEHwiK8ANBEX4gKMIPBMX1/MPc4sWLk/X7778/WZ86dWqR7QzKiBEjKlt3BGz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo3Ov5zWylpFmSDrj7lGzaGEkvSJooqVvSLe7+Re7KuJ6/7Zx55pnJet698S+66KIi2/mRNWvWJOs33XRTaeseyoq8nv9pSceOnnCvpNfd/VxJr2evAQwhueF3942Sjh0yZrakVdnzVZJuKLgvACVr9Dv/OHffJ0nZ49jiWgLQCqWf229mXZK6yl4PgMFpdMu/38w6JCl7PFDrje6+3N073b2zwXUBKEGj4V8naW72fK6kl4tpB0Cr5IbfzFZL+q+kX5pZj5n9UdJDkmaY2U5JM7LXAIaQ3O/87j6nRml6wb2gBLfffnuynnff/jLvy59n06ZNla07As7wA4Ii/EBQhB8IivADQRF+ICjCDwTFEN1DwPnnn5+sr127tmbtnHPOSc57/PHte/d2huhuDEN0A0gi/EBQhB8IivADQRF+ICjCDwRF+IGg2vcgL35wwQUXJOuTJk2qWWvn4/h57rnnnmT9rrvualEnwxNbfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IaugeBA4kdb2+JC1YsKBm7eGHH07Oe9JJJzXUUyt0dHRU3cKwxpYfCIrwA0ERfiAowg8ERfiBoAg/EBThB4LKPc5vZislzZJ0wN2nZNMWSfqTpP9lb7vP3f9VVpNIW7ZsWc3azp07k/OOHj26qXXn3S/gscceq1k79dRTm1o3mlPPlv9pSTMHmP53d5+a/SP4wBCTG3533yiptwW9AGihZr7z32lm75rZSjM7rbCOALREo+F/QtJkSVMl7ZO0pNYbzazLzLaY2ZYG1wWgBA2F3933u/thdz8i6UlJlyXeu9zdO929s9EmARSvofCbWf/LrW6UtK2YdgC0Sj2H+lZLukbSGWbWI2mhpGvMbKokl9QtaV6JPQIogbl761Zm1rqVoSXM0kPBL1q0qGbtgQceSM778ccfJ+vTp09P1j/55JNkfbhy9/R/lAxn+AFBEX4gKMIPBEX4gaAIPxAU4QeC4tbdaMqIESOS9bzDeSnfffddsn748OGGlw22/EBYhB8IivADQRF+ICjCDwRF+IGgCD8QFMf50ZTFixeXtuwVK1Yk6z09PaWtOwK2/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFLfurtPpp59es/bUU08l5129enVT9Sp1dHQk6x988EGy3sww3JMnT07Wd+/e3fCyhzNu3Q0gifADQRF+ICjCDwRF+IGgCD8QFOEHgsq9nt/Mxkt6RtKZko5IWu7u/zCzMZJekDRRUrekW9z9i/JardayZctq1q6//vrkvOedd16yvnfv3mT9s88+S9Z37dpVs3bJJZck583rbcGCBcl6M8fxlyxZkqznfS5oTj1b/u8lzXf3CyRdLukOM7tQ0r2SXnf3cyW9nr0GMETkht/d97n729nzg5J2SDpL0mxJq7K3rZJ0Q1lNAijeoL7zm9lESdMkvSlpnLvvk/r+QEgaW3RzAMpT9z38zGykpDWS7nb3r83qOn1YZtYlqaux9gCUpa4tv5mdoL7gP+vuL2WT95tZR1bvkHRgoHndfbm7d7p7ZxENAyhGbvitbxO/QtIOd1/ar7RO0tzs+VxJLxffHoCy5F7Sa2ZXSXpD0nvqO9QnSfep73v/i5ImSNoj6WZ3781Z1pC9pPfyyy+vWVu6dGnNmiRdccUVTa27u7s7Wd++fXvN2tVXX52cd9SoUY209IO8/39Sl/xeeumlyXm//fbbhnqKrt5LenO/87v7Jkm1FjZ9ME0BaB+c4QcERfiBoAg/EBThB4Ii/EBQhB8Iilt3FyDv0tTUJbeS9PjjjxfZTkv19iZP7Uje8hzl4NbdAJIIPxAU4QeCIvxAUIQfCIrwA0ERfiCoum/jhdrmz5+frJ944onJ+siRI5ta/7Rp02rW5syZ09Syv/rqq2R9xowZTS0f1WHLDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBcT0/MMxwPT+AJMIPBEX4gaAIPxAU4QeCIvxAUIQfCCo3/GY23sw2mNkOM3vfzP6cTV9kZp+Z2dbs3+/KbxdAUXJP8jGzDkkd7v62mY2S9JakGyTdIukbd3+k7pVxkg9QunpP8sm9k4+775O0L3t+0Mx2SDqrufYAVG1Q3/nNbKKkaZLezCbdaWbvmtlKMzutxjxdZrbFzLY01SmAQtV9br+ZjZT0H0kPuvtLZjZO0ueSXNJf1ffV4A85y2C3HyhZvbv9dYXfzE6Q9IqkV9196QD1iZJecfcpOcsh/EDJCruwx8xM0gpJO/oHP/sh8KgbJW0bbJMAqlPPr/1XSXpD0nuSjmST75M0R9JU9e32d0ual/04mFoWW36gZIXu9heF8APl43p+AEmEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoHJv4FmwzyV90u/1Gdm0dtSuvbVrXxK9NarI3n5e7xtbej3/T1ZutsXdOytrIKFde2vXviR6a1RVvbHbDwRF+IGgqg7/8orXn9KuvbVrXxK9NaqS3ir9zg+gOlVv+QFUpJLwm9lMM/vQzHaZ2b1V9FCLmXWb2XvZyMOVDjGWDYN2wMy29Zs2xsxeM7Od2eOAw6RV1FtbjNycGFm60s+u3Ua8bvluv5kdJ+kjSTMk9UjaLGmOu29vaSM1mFm3pE53r/yYsJn9WtI3kp45OhqSmf1NUq+7P5T94TzN3f/SJr0t0iBHbi6pt1ojS/9eFX52RY54XYQqtvyXSdrl7rvd/ZCk5yXNrqCPtufuGyX1HjN5tqRV2fNV6vufp+Vq9NYW3H2fu7+dPT8o6ejI0pV+dom+KlFF+M+S9Gm/1z1qryG/XdJ6M3vLzLqqbmYA446OjJQ9jq24n2PljtzcSseMLN02n10jI14XrYrwDzSaSDsdcrjS3X8l6beS7sh2b1GfJyRNVt8wbvskLamymWxk6TWS7nb3r6vspb8B+qrkc6si/D2Sxvd7fbakvRX0MSB335s9HpC0Vn1fU9rJ/qODpGaPByru5wfuvt/dD7v7EUlPqsLPLhtZeo2kZ939pWxy5Z/dQH1V9blVEf7Nks41s0lmNkLSbZLWVdDHT5jZKdkPMTKzUyRdp/YbfXidpLnZ87mSXq6wlx9pl5Gba40srYo/u3Yb8bqSk3yyQxmPSjpO0kp3f7DlTQzAzH6hvq291HfF43NV9mZmqyVdo76rvvZLWijpn5JelDRB0h5JN7t7y394q9HbNRrkyM0l9VZrZOk3VeFnV+SI14X0wxl+QEyc4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/A+Rq/ARM9qglAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dSD+c4QfExBl+QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8ZCToN0NMbAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADUFJREFUeJzt3X+oXPWZx/HPZ2MSweSPSDEbrLtpaljW35WgorJmEUOMhRihUiVLZMumQoQtrLqa/FFBq7JuurtCiKQ0NpXUpkRdQ1mSBlnWLCxilNiYX63UbHvNJT+0kFTREPPsH/dkuY13vjOZX2fufd4vkJk5z5w5j6Of+z0zZ875OiIEIJ8/qbsBAPUg/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkjqvnxuzzc8JgR6LCLfyvI5GftsLbR+w/Z7tRzp5LQD95XZ/2297kqRfSbpN0pCkNyXdExF7C+sw8gM91o+R/zpJ70XEbyLipKSfSlrcwesB6KNOwn+xpN+NejxULfsjtpfb3ml7ZwfbAtBlnXzhN9auxRd26yNinaR1Erv9wCDpZOQfknTJqMdflnSos3YA9Esn4X9T0lzbX7E9RdI3JW3pTlsAeq3t3f6IOGX7AUnbJE2StD4i9nStMwA91fahvrY2xmd+oOf68iMfAOMX4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSfX10t3IZ86cOQ1rTz31VHHdJUuWFOtXXXVVsb5///5iPTtGfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IiuP86MiNN95YrG/durVh7ejRo8V116xZU6wfPny4WEcZIz+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJNXRcX7bByWdkPS5pFMRMa8bTWFw3HHHHcX65s2bi/XnnnuuYW3VqlXFdT/55JNiHZ3pxo98/joijnXhdQD0Ebv9QFKdhj8k/cL2W7aXd6MhAP3R6W7/TRFxyPZFkrbb3h8Rr49+QvVHgT8MwIDpaOSPiEPV7RFJr0i6boznrIuIeXwZCAyWtsNv+wLb08/cl7RA0rvdagxAb3Wy2z9T0iu2z7zOTyKi8fmbAAaKI6J/G7P7tzG05NJLLy3W33nnnWJ9x44dxfqiRYsa1k6fPl1cF+2JCLfyPA71AUkRfiApwg8kRfiBpAg/kBThB5LiUN8Ed/755xfrpUtrt7L+ggULivXjx48X6+g+DvUBKCL8QFKEH0iK8ANJEX4gKcIPJEX4gaSYonuCe/zxx4v166+/vlifO3dusc5x/PGLkR9IivADSRF+ICnCDyRF+IGkCD+QFOEHkuJ8/glg6tSpDWsHDx4srrtr165i/fbbb2+nJdSI8/kBFBF+ICnCDyRF+IGkCD+QFOEHkiL8QFJNz+e3vV7S1yUdiYgrqmUXStokabakg5Lujojf965NlDz88MMNa9OmTSuuu2rVqm63g3GilZH/R5IWnrXsEUmvRcRcSa9VjwGMI03DHxGvS/rorMWLJW2o7m+QdGeX+wLQY+1+5p8ZEcOSVN1e1L2WAPRDz6/hZ3u5pOW93g6Ac9PuyH/Y9ixJqm6PNHpiRKyLiHkRMa/NbQHogXbDv0XSsur+MkmvdqcdAP3SNPy2X5T0P5L+wvaQ7W9JelrSbbZ/Lem26jGAcYTz+SeAHTt2NKx9/PHHxXUXLjz7KC7GO87nB1BE+IGkCD+QFOEHkiL8QFKEH0iKKbrHgZtvvrlYv+GGGxrWrrzyym63c07mz5/fsHb06NHiunv27OlyNxiNkR9IivADSRF+ICnCDyRF+IGkCD+QFOEHkuI4/ziwdOnSYn3fvn0Na++//35H277vvvuK9dWrVxfrM2bMaFj77LPPius++OCDxfqaNWuKdZQx8gNJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUly6exw4efJksX7vvfc2rG3evLm47pQpU4r1AwcOFOsPPfRQsb5t27aGtUWLFhXXff7554v1u+66q1jfunVrsT5RceluAEWEH0iK8ANJEX4gKcIPJEX4gaQIP5BU0/P5ba+X9HVJRyLiimrZY5L+TtKZC6+vjIj/6FWTE93ll19erJ93Xvk/06lTp9re9rXXXlusNztW3ux3BCWbNm0q1pvNV/Doo48W61mP87eqlZH/R5LGmsT9XyLimuofgg+MM03DHxGvS/qoD70A6KNOPvM/YPuXttfbbnytJgADqd3wr5X0VUnXSBqW1PBCbraX295pe2eb2wLQA22FPyIOR8TnEXFa0g8kXVd47rqImBcR89ptEkD3tRV+27NGPVwi6d3utAOgX1o51PeipPmSvmR7SNJ3Jc23fY2kkHRQ0rd72COAHuB8/gFw6623Fuvbt28v1i+77LKGtf379xfXnT59erHe7Hz/Dz/8sFjvROnfS5J2795drE+aNKmb7YwbnM8PoIjwA0kRfiApwg8kRfiBpAg/kBRTdE8AH3zwQdvrnjhxoouddNfQ0FDdLUxojPxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBTH+QeAXT4Ds1l9orrllluK9UH+jcJ4wMgPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0lxnH8ANLt8ej8vr95PkydPLtbvv//+Yv2FF17oZjvpMPIDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFJNj/PbvkTSjyX9qaTTktZFxL/ZvlDSJkmzJR2UdHdE/L53rU5ce/fuLdaHh4eL9aVLlzasrV27tq2euqV0LL9Zb7Nnzy7Wly1b1k5LqLQy8p+S9A8R8ZeSbpC0wvZlkh6R9FpEzJX0WvUYwDjRNPwRMRwRb1f3T0jaJ+liSYslbaietkHSnb1qEkD3ndNnftuzJX1N0huSZkbEsDTyB0LSRd1uDkDvtPzbftvTJL0k6TsRcbzV68rZXi5peXvtAeiVlkZ+25M1EvyNEfFytfiw7VlVfZakI2OtGxHrImJeRMzrRsMAuqNp+D0yxP9Q0r6I+P6o0hZJZ75uXSbp1e63B6BXWtntv0nS30jabXtXtWylpKcl/cz2tyT9VtI3etPixNfsUN6TTz5ZrK9evbrtbW/cuLFYnzNnTrF+9dVXF+srV65sWPv000+L6y5YsKBYP3bsWLGOsqbhj4j/ltToA/6t3W0HQL/wCz8gKcIPJEX4gaQIP5AU4QeSIvxAUu7nZaFtT8xrUNdsxYoVDWvPPPNMcd2pU6d2tO1m02Q/++yzDWtPPPFEcd2TJ0+21VN2EdHSb+8Z+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKY7zAxMMx/kBFBF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUk3Db/sS2/9pe5/tPbb/vlr+mO0PbO+q/lnU+3YBdEvTi3nYniVpVkS8bXu6pLck3Snpbkl/iIh/bnljXMwD6LlWL+ZxXgsvNCxpuLp/wvY+SRd31h6Aup3TZ37bsyV9TdIb1aIHbP/S9nrbMxqss9z2Tts7O+oUQFe1fA0/29Mk/Zek70XEy7ZnSjomKSQ9rpGPBn/b5DXY7Qd6rNXd/pbCb3uypJ9L2hYR3x+jPlvSzyPiiiavQ/iBHuvaBTxtW9IPJe0bHfzqi8Azlkh691ybBFCfVr7tv1nSDkm7JZ2uFq+UdI+kazSy239Q0rerLwdLr8XID/RYV3f7u4XwA73HdfsBFBF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSanoBzy47Jul/Rz3+UrVsEA1qb4Pal0Rv7epmb3/e6hP7ej7/FzZu74yIebU1UDCovQ1qXxK9tauu3tjtB5Ii/EBSdYd/Xc3bLxnU3ga1L4ne2lVLb7V+5gdQn7pHfgA1qSX8thfaPmD7PduP1NFDI7YP2t5dzTxc6xRj1TRoR2y/O2rZhba32/51dTvmNGk19TYQMzcXZpau9b0btBmv+77bb3uSpF9Juk3SkKQ3Jd0TEXv72kgDtg9KmhcRtR8Ttv1Xkv4g6cdnZkOy/U+SPoqIp6s/nDMi4h8HpLfHdI4zN/eot0YzS9+nGt+7bs543Q11jPzXSXovIn4TEScl/VTS4hr6GHgR8bqkj85avFjShur+Bo38z9N3DXobCBExHBFvV/dPSDozs3St712hr1rUEf6LJf1u1OMhDdaU3yHpF7bfsr287mbGMPPMzEjV7UU193O2pjM399NZM0sPzHvXzozX3VZH+MeaTWSQDjncFBHXSrpd0opq9xatWSvpqxqZxm1Y0uo6m6lmln5J0nci4nidvYw2Rl+1vG91hH9I0iWjHn9Z0qEa+hhTRByqbo9IekUjH1MGyeEzk6RWt0dq7uf/RcThiPg8Ik5L+oFqfO+qmaVfkrQxIl6uFtf+3o3VV13vWx3hf1PSXNtfsT1F0jclbamhjy+wfUH1RYxsXyBpgQZv9uEtkpZV95dJerXGXv7IoMzc3GhmadX83g3ajNe1/MinOpTxr5ImSVofEd/rexNjsD1HI6O9NHLG40/q7M32i5Lma+Ssr8OSvivp3yX9TNKfSfqtpG9ERN+/eGvQ23yd48zNPeqt0czSb6jG966bM153pR9+4QfkxC/8gKQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8k9X/RRfk8PXzIkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DYuIIPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0/e89J2jjtINIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BEX4gqP8D/gkxb+1Oq5EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kfdfdW/7DW05vc3WSMzc3qbe8maW3qsLPrswZr0vphyP8gJg4wg8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFD/D2YJUbWCzmzFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X0g/f8ANi4ht+QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeC+j/CY25YYhdmXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADXZJREFUeJzt3X+IVXUax/HPs2YEWmRINpRmSdQuQroMtZBsrmHMLoEJGc4/ueyy0x9FWyyxFUTBEMiytS1BxYSiQmZCtmptv5iWrYWIJq2mtExk1lxlZs3AqSgbffaPOS6Tzf3eO/eec88dn/cL5N57nnvufbj4mXPO/Z5zv+buAhDPj8puAEA5CD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaDOaOabmRmnEwIFc3er5XkNbfnNrMPMPjGzvWZ2TyOvBaC5rN5z+81siqQ9kpZKOiDpHUmd7r4rsQ5bfqBgzdjyXyVpr7vvc/djkjZJWtbA6wFookbCf6Gkz8Y8PpAt+x4z6zKzPjPra+C9AOSskS/8xtu1+MFuvbv3SOqR2O0HWkkjW/4DkmaPeXyRpIONtQOgWRoJ/zuSLjOzS8zsTEkrJW3Lpy0ARat7t9/dR8zsdkmvSJoiaa27f5RbZwAKVfdQX11vxjE/ULimnOQDYPIi/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCopk7RjfpMnz49WZ8/f37F2k033ZRc9+jRo8n6woULk/W2trZk/cknn6xY27BhQ3LdEydOJOtoDFt+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiqoVl6zWxA0rCk45JG3L29yvNDztI7b968ZL27uztZ7+joSNbPPffcirVvvvkmue7IyEiyPm3atGT922+/TdbPOuusirWlS5cm1+3t7U3WMb5aZ+nN4ySfX7j74RxeB0ATsdsPBNVo+F3Sq2b2rpl15dEQgOZodLf/Gnc/aGbnS3rNzD529zfGPiH7o8AfBqDFNLTld/eD2e2QpOclXTXOc3rcvb3al4EAmqvu8JvZNDM7++R9SddL+jCvxgAUq5Hd/lmSnjezk6+z0d1fzqUrAIVraJx/wm8WdJz/5ZfTfxOrXbe+d+/eZP3zzz+vWHvrrbeS63788cfJ+jnnnJOsVzuPYPv27XW/9/Lly5N1jK/WcX6G+oCgCD8QFOEHgiL8QFCEHwiK8ANBMdTXBHPmzEnW9+/f36ROmm/nzp0Va5dffnly3QsuuCBZr/az41Ex1AcgifADQRF+ICjCDwRF+IGgCD8QFOEHgmKK7iY4ncfxr7766mQ9NX34li1bkusODw/X1RNqw5YfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Lien4kVZuiu6+vL1mfMWNGxVrqHABJOnyYyZ/rwfX8AJIIPxAU4QeCIvxAUIQfCIrwA0ERfiCoqtfzm9laSTdIGnL3+dmy8yQ9K2mupAFJN7v7F8W1iaLMnDkzWd+8eXOyPm/evGR9yZIlFWuM45erli3/Okkdpyy7R1Kvu18mqTd7DGASqRp+d39D0pFTFi+TtD67v17SjTn3BaBg9R7zz3L3Q5KU3Z6fX0sAmqHw3/Azsy5JXUW/D4CJqXfLP2hmbZKU3Q5VeqK797h7u7u31/leAApQb/i3SVqV3V8laWs+7QBolqrhN7NnJL0l6XIzO2Bmv5W0WtJSM/tU0tLsMYBJhOv5TwOpeexvueWW5LorV65M1hcsWJCsHzt2LFl//PHHK9b27duXXHfjxo3J+pEjpw5CQeJ6fgBVEH4gKMIPBEX4gaAIPxAU4QeCYqhvErj22muT9XXr1lWsXXzxxTl30zz9/f3J+pVXXtmkTiYXhvoAJBF+ICjCDwRF+IGgCD8QFOEHgiL8QFCF/4wXGvfVV18l6zt37qxY27BhQ3LdapfVbt1a3O+0dHZ2JuuPPvposn7//fcn693d3RPuKRK2/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFNfzo2Vt3749WV+0aFGyPmPGjDzbmTS4nh9AEuEHgiL8QFCEHwiK8ANBEX4gKMIPBFX1en4zWyvpBklD7j4/W/agpN9J+m/2tPvc/e9FNYmY1qxZk6xXG+dHWi1b/nWSOsZZ/hd3X5D9I/jAJFM1/O7+hqQjTegFQBM1csx/u5l9YGZrzSzmeZTAJFZv+J+QNE/SAkmHJD1c6Ylm1mVmfWbWV+d7AShAXeF390F3P+7uJyQ9JemqxHN73L3d3dvrbRJA/uoKv5m1jXm4XNKH+bQDoFlqGep7RtJiSTPN7ICkByQtNrMFklzSgKRbC+wRQAGqht/dx/tx9fQALNAEZ5yR/u87c+bMirXDhw/n3c6kwxl+QFCEHwiK8ANBEX4gKMIPBEX4gaCYohstKzVUJ0kjIyPJOsN5aWz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAopuhGyxoaGkrWp06dmqwzRXcaW34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrr+XNQ7SekV69enazfe++9yfp333034Z5axZQpUyrWHnvsseS61a7n7+7urqsnjGLLDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBVb2e38xmS9og6QJJJyT1uPtfzew8Sc9KmitpQNLN7v5Fldc6La/nX7x4cbL++uuvJ+svvfRSsn7XXXcl63v27EnWi3TppZcm6z09PRVrS5YsSa7b39+frF933XXJetTf7c/zev4RSX9w9x9L+pmk28zsJ5LukdTr7pdJ6s0eA5gkqobf3Q+5+47s/rCk3ZIulLRM0vrsaesl3VhUkwDyN6FjfjObK2mhpLclzXL3Q9LoHwhJ5+fdHIDi1Hxuv5lNl/ScpDvd/ahZTYcVMrMuSV31tQegKDVt+c1sqkaD/7S7b8kWD5pZW1ZvkzTury26e4+7t7t7ex4NA8hH1fDb6CZ+jaTd7v7ImNI2Sauy+6skbc2/PQBFqWWob5GkNyX1a3SoT5Lu0+hx/2ZJcyTtl7TC3Y9Uea3Tcqhv+vTpyfquXbuS9dmzZyfrAwMDyXrqkuBqw12LFi1K1qv1tmLFimQ99dm8//77yXU7OjqS9cHBwWQ9qlqH+qoe87v7vyRVerH0QCuAlsUZfkBQhB8IivADQRF+ICjCDwRF+IGgmKK7CebPn5+sb9y4saH1i1TtNO5q/396e3sr1u6+++7kuu+9916yjvExRTeAJMIPBEX4gaAIPxAU4QeCIvxAUIQfCIpx/hZwxRVXJOudnZ3J+h133FGx9vXXXyfX3bFjR7K+adOmZP3FF19M1oeHhyvWjh8/nlwX9WGcH0AS4QeCIvxAUIQfCIrwA0ERfiAowg8ExTg/cJphnB9AEuEHgiL8QFCEHwiK8ANBEX4gKMIPBFU1/GY228z+YWa7zewjM/t9tvxBM/uPmb2X/ftV8e0CyEvVk3zMrE1Sm7vvMLOzJb0r6UZJN0v60t3/XPObcZIPULhaT/I5o4YXOiTpUHZ/2Mx2S7qwsfYAlG1Cx/xmNlfSQklvZ4tuN7MPzGytmc2osE6XmfWZWV9DnQLIVc3n9pvZdEn/lPSQu28xs1mSDktySd0aPTT4TZXXYLcfKFitu/01hd/Mpkp6QdIr7v7IOPW5kl5w9+SMkoQfKF5uF/bY6DStayTtHhv87IvAk5ZL+nCiTQIoTy3f9i+S9KakfkknssX3SeqUtECju/0Dkm7NvhxMvRZbfqBgue7254XwA8Xjen4ASYQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgqv6AZ84OS/r3mMczs2WtqFV7a9W+JHqrV569XVzrE5t6Pf8P3tysz93bS2sgoVV7a9W+JHqrV1m9sdsPBEX4gaDKDn9Pye+f0qq9tWpfEr3Vq5TeSj3mB1Cesrf8AEpSSvjNrMPMPjGzvWZ2Txk9VGJmA2bWn808XOoUY9k0aENm9uGYZeeZ2Wtm9ml2O+40aSX11hIzNydmli71s2u1Ga+bvttvZlMk7ZG0VNIBSe9I6nT3XU1tpAIzG5DU7u6ljwmb2c8lfSlpw8nZkMzsT5KOuPvq7A/nDHf/Y4v09qAmOHNzQb1Vmln61yrxs8tzxus8lLHlv0rSXnff5+7HJG2StKyEPlqeu78h6cgpi5dJWp/dX6/R/zxNV6G3luDuh9x9R3Z/WNLJmaVL/ewSfZWijPBfKOmzMY8PqLWm/HZJr5rZu2bWVXYz45h1cmak7Pb8kvs5VdWZm5vplJmlW+azq2fG67yVEf7xZhNppSGHa9z9p5J+Kem2bPcWtXlC0jyNTuN2SNLDZTaTzSz9nKQ73f1omb2MNU5fpXxuZYT/gKTZYx5fJOlgCX2My90PZrdDkp7X6GFKKxk8OUlqdjtUcj//5+6D7n7c3U9IekolfnbZzNLPSXra3bdki0v/7Mbrq6zPrYzwvyPpMjO7xMzOlLRS0rYS+vgBM5uWfREjM5sm6Xq13uzD2yStyu6vkrS1xF6+p1Vmbq40s7RK/uxabcbrUk7yyYYyHpU0RdJad3+o6U2Mw8wu1ejWXhq94nFjmb2Z2TOSFmv0qq9BSQ9I+pukzZLmSNovaYW7N/2Ltwq9LdYEZ24uqLdKM0u/rRI/uzxnvM6lH87wA2LiDD8gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0H9D2HtO/RQQAnmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MbKukTg1c9XVc0o8kvSbpF5KmSfqzpO+6e8vfeKvRW6eGOXNzSb3Vmln6PVW474qc8bqQfviEHxATn/ADgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxDU/wJFP1ZHhdAvJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADaZJREFUeJzt3X/oVfUdx/HXe6YE3yy0kZqr6VbElpE/vkjQGI5wuSFoVP4IylXsu6Jiwv6Y9U/RCCL2EwTBNfFrmFakU2RMlwwtWNE3i3JzbTG+06+aX8UohfBHvvfH9zi+6fd+7vXcc8+5X9/PB8i997zvOefdodf3nHvPuedj7i4A8Xyl6gYAVIPwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6pIyV2ZmXE4ItJi7WyPva2rPb2ZzzOxDM/vIzJY1sywA5bK81/ab2QhJ/5I0W1KfpLclLXb3fyTmYc8PtFgZe/6Zkj5y9/+4+0lJ6yXNa2J5AErUTPgnSto36HVfNu1LzKzLzHrMrKeJdQEoWDNf+A11aHHeYb27r5S0UuKwH2gnzez5+yRdM+j11yQdaK4dAGVpJvxvS7rezCab2ShJiyRtLqYtAK2W+7Df3U+b2aOStkoaIWmVu/+9sM4AtFTuU325VsZnfqDlSrnIB8DwRfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQuYfoliQz65V0TNIXkk67e2cRTUXT2ZnebD09PSV1MrzcddddyfqOHTtq1g4fPlx0O8NOU+HPfM/djxSwHAAl4rAfCKrZ8LukbWb2jpl1FdEQgHI0e9h/q7sfMLOrJP3FzP7p7jsHvyH7o8AfBqDNNLXnd/cD2WO/pI2SZg7xnpXu3smXgUB7yR1+M+sws9Fnn0v6vqTdRTUGoLWaOewfJ2mjmZ1dzovu/udCugLQcubu5a3MrLyVtZHbb789WV+7dm2y3t/fn6zffPPNNWunTp1KztvOFi1alKx3d3cn65s2bapZW7BgQa6ehgN3t0bex6k+ICjCDwRF+IGgCD8QFOEHgiL8QFBF/KovvDlz5iTrL7zwQrI+duzYpurZtRYXnRMnTiTrZ86cSdZnz55ds7ZmzZrkvPfdd1+yfjFgzw8ERfiBoAg/EBThB4Ii/EBQhB8IivADQXGev0EdHR01a08//XRy3iuvvDJZr3e+esWKFcn66dOnk/XhauPGjcn6gQMHkvXJkyfXrN1yyy3JeUePHp2sHzt2LFkfDtjzA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQnOdv0PLly2vW6g2xXU+935Y/9thjTS0f57vuuuuS9TvvvDNZX716dYHdVIM9PxAU4QeCIvxAUIQfCIrwA0ERfiAowg8EVfc8v5mtkjRXUr+7T8mmjZX0kqRJknolLXD3T1rXZutNnz49WZ87d27uZR89ejRZT11DALRKI3v+1ZLOHZVimaTt7n69pO3ZawDDSN3wu/tOSefuuuZJ6s6ed0uaX3BfAFos72f+ce5+UJKyx6uKawlAGVp+bb+ZdUnqavV6AFyYvHv+Q2Y2QZKyx/5ab3T3le7e6e7N/foFQKHyhn+zpCXZ8yWSNhXTDoCy1A2/ma2T9DdJN5hZn5k9KOlZSbPN7N+SZmevAQwj5u7lrcysvJWdY8aMGcn69u3bk/XLL78897oXLlyYrL/yyiu5lx1Zd3d3sn7vvffmXvbHH3+crF999dW5l91q7m6NvI8r/ICgCD8QFOEHgiL8QFCEHwiK8ANBhbl192233ZasN3Mq7913303Wt2zZknvZqO2hhx5K1m+66aaatalTpybnHTFiRK6ehhP2/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QVJjz/EuXLm1q/iNHjtSsPf7448l5P//886bWjaHV264nT57MvexRo0Yl65MmTUrWe3t7c6+7LOz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCoMOf5J0yYkKzXu4X5m2++WbO2bdu2XD1FN378+Kbq9XR0dOSe94orrkjWt27dmqzfcMMNudddFvb8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxBU3SG6zWyVpLmS+t19SjbtKUk/lnQ4e9sT7v6nuiurcIjuev+d9er79++vWVu7dm1y3uXLlyfrrTRt2rRkffHixSV1cr4bb7wxWU/dd79qn376abI+ZsyYkjo5X5FDdK+WNGeI6b9x96nZv7rBB9Be6obf3XdKOlpCLwBK1Mxn/kfN7H0zW2Vm1R3jAMglb/hXSPqmpKmSDkr6Va03mlmXmfWYWU/OdQFogVzhd/dD7v6Fu5+R9HtJMxPvXenune7embdJAMXLFX4zG/wTuTsk7S6mHQBlqfuTXjNbJ2mWpK+aWZ+kJyXNMrOpklxSr6SftLBHAC1Q9zx/oSur8Dz/hg0bkvX58+eX1AmGg+PHjyfrs2bNStZ37dpVYDcXpsjz/AAuQoQfCIrwA0ERfiAowg8ERfiBoMKc6qtn3bp1yfrChQtL6iSO119/PVnfsWNHU8u///77a9YmTpyYnHf9+vXJ+j333JOrpzJwqg9AEuEHgiL8QFCEHwiK8ANBEX4gKMIPBMV5/szIkSOT9UsvvbRm7eGHH07OO3ny5Fw9NeqNN96oWdu3b19y3hkzZiTrzz//fK6eGnHy5Mlk/cSJE00tPzWs+syZNW8+JUl64IEHkvXVq1fnaakUnOcHkET4gaAIPxAU4QeCIvxAUIQfCIrwA0HVvW9/FKdOncpdf+6554pupzQ7d+6suoXcpkyZkqxfe+21JXUyPLHnB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGg6p7nN7NrJK2RNF7SGUkr3f13ZjZW0kuSJknqlbTA3T9pXavAl+3evTtZ37t3b83a+PHji25n2Glkz39a0s/c/VuSbpH0iJl9W9IySdvd/XpJ27PXAIaJuuF394Puvit7fkzSHkkTJc2T1J29rVvS/FY1CaB4F/SZ38wmSZom6S1J49z9oDTwB0LSVUU3B6B1Gr6238wuk/SqpKXu/plZQ7cJk5l1SerK1x6AVmloz29mIzUQ/LXuviGbfMjMJmT1CZL6h5rX3Ve6e6e7dxbRMIBi1A2/Dezi/yBpj7v/elBps6Ql2fMlkjYV3x6AVmnksP9WSfdK+sDM3sumPSHpWUkvm9mDkvZKurs1LQL59PcPeTAqSerp6UnO+9prrxXdTtupG353f0NSrQ/4txXbDoCycIUfEBThB4Ii/EBQhB8IivADQRF+ICiG6MZFK/Wz3UsuSZ/l7uvrK7qd0jBEN4Akwg8ERfiBoAg/EBThB4Ii/EBQhB8IivP8wEWG8/wAkgg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gqLrhN7NrzOyvZrbHzP5uZj/Npj9lZvvN7L3s3w9b3y6AotS9mYeZTZA0wd13mdloSe9Imi9pgaTj7v7LhlfGzTyAlmv0Zh7pYUsGFnRQ0sHs+TEz2yNpYnPtAajaBX3mN7NJkqZJeiub9KiZvW9mq8xsTI15usysx8x6muoUQKEavoefmV0maYekZ9x9g5mNk3REkkv6hQY+GjxQZxkc9gMt1uhhf0PhN7ORkrZI2uruvx6iPknSFnefUmc5hB9oscJu4GlmJukPkvYMDn72ReBZd0jafaFNAqhOI9/2f0fS65I+kHQmm/yEpMWSpmrgsL9X0k+yLwdTy2LPD7RYoYf9RSH8QOtx334ASYQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGg6t7As2BHJP130OuvZtPaUbv21q59SfSWV5G9fb3RN5b6e/7zVm7W4+6dlTWQ0K69tWtfEr3lVVVvHPYDQRF+IKiqw7+y4vWntGtv7dqXRG95VdJbpZ/5AVSn6j0/gIpUEn4zm2NmH5rZR2a2rIoeajGzXjP7IBt5uNIhxrJh0PrNbPegaWPN7C9m9u/scchh0irqrS1Gbk6MLF3ptmu3Ea9LP+w3sxGS/iVptqQ+SW9LWuzu/yi1kRrMrFdSp7tXfk7YzL4r6bikNWdHQzKz5yQddfdnsz+cY9z9523S21O6wJGbW9RbrZGlf6QKt12RI14XoYo9/0xJH7n7f9z9pKT1kuZV0Efbc/edko6eM3mepO7sebcG/ucpXY3e2oK7H3T3XdnzY5LOjixd6bZL9FWJKsI/UdK+Qa/71F5DfrukbWb2jpl1Vd3MEMadHRkpe7yq4n7OVXfk5jKdM7J022y7PCNeF62K8A81mkg7nXK41d2nS/qBpEeyw1s0ZoWkb2pgGLeDkn5VZTPZyNKvSlrq7p9V2ctgQ/RVyXarIvx9kq4Z9Pprkg5U0MeQ3P1A9tgvaaMGPqa0k0NnB0nNHvsr7uf/3P2Qu3/h7mck/V4VbrtsZOlXJa119w3Z5Mq33VB9VbXdqgj/25KuN7PJZjZK0iJJmyvo4zxm1pF9ESMz65D0fbXf6MObJS3Jni+RtKnCXr6kXUZurjWytCredu024nUlF/lkpzJ+K2mEpFXu/kzpTQzBzL6hgb29NPCLxxer7M3M1kmapYFffR2S9KSkP0p6WdK1kvZKutvdS//irUZvs3SBIze3qLdaI0u/pQq3XZEjXhfSD1f4ATFxhR8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaD+ByrkCO9fWNIsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADa1JREFUeJzt3WuMVPUZx/Hfo6LGW4IacAUUL9hUSCrNaiqaegtIGyNKxMirbVq7JmpSTF8U9AUmjUZrtfUVCUYUE7kZEYlpCgSbcrEiF4kiVEWlsl2yK8FE8IUGefpizzYr7vzP7MyZOcM+309CZuY8c855MuG358z8z8zf3F0A4jmp7AYAlIPwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6pRm7szMuJwQaDB3t2qeV9eR38ymm9mHZrbXzObWsy0AzWW1XttvZidL+kjSVEldkrZKmu3uuxPrcOQHGqwZR/5rJO1190/d/VtJyyTNqGN7AJqonvCPkbR/wOOubNn3mFmnmW0zs2117AtAwer5wG+wU4sfnNa7+0JJCyVO+4FWUs+Rv0vSuAGPx0rqrq8dAM1ST/i3SppgZpeY2amS7pG0upi2ADRazaf97n7UzB6UtEbSyZIWufsHhXUGoKFqHuqraWe85wcarikX+QA4cRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EFRTp+jG8DNu3Lhkff369RVrEyZMSK77xRdfJOs333xzsr5r165kPTqO/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QVF2z9JrZPkmHJX0n6ai7t+c8n1l6TzAXX3xxsr5mzZpkPW8svx6ffPJJsn7FFVc0bN+trNpZeou4yOcmdz9YwHYANBGn/UBQ9YbfJa01s+1m1llEQwCao97T/uvcvdvMRklaZ2b/dvcNA5+Q/VHgDwPQYuo68rt7d3bbK+k1SdcM8pyF7t6e92EggOaqOfxmdqaZnd1/X9I0SXyNCjhB1HPaP1rSa2bWv50l7v73QroC0HA1h9/dP5X0kwJ7QQmmTp2arK9cuTJZP+OMM2re91tvvZWsT5kyJVk/6SQGq+rBqwcERfiBoAg/EBThB4Ii/EBQhB8Iip/uHubGjx+frD/77LPJej1DeZK0c+fOirVp06Yl1126dGmyPmnSpJp6Qh+O/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QVF0/3T3knfHT3U23cePGZD3va7N5enp6kvWZM2dWrL399tvJdUeOHJmsjxgxIlnv7e1N1oeran+6myM/EBThB4Ii/EBQhB8IivADQRF+ICjCDwTF9/mHgXnz5lWsXX311XVtO28c/6677krW88byU7788sua10U+jvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EFTuOL+ZLZJ0m6Red5+ULTtX0nJJ4yXtk3S3uzMo2yC33357sj5//vyKtbzvvOdZsWJFsp43zTZaVzVH/hclTT9u2VxJ6919gqT12WMAJ5Dc8Lv7BkmHjls8Q9Li7P5iSXcU3BeABqv1Pf9odz8gSdntqOJaAtAMDb+238w6JXU2ej8AhqbWI3+PmbVJUnZb8ZcS3X2hu7e7e3uN+wLQALWGf7Wkjux+h6TXi2kHQLPkht/Mlkr6l6QfmVmXmf1G0hOSpprZx5KmZo8BnEBy3/O7++wKpVsK7gUVnHPOOcl6PWP5mzdvTtYfeeSRmreN1sYVfkBQhB8IivADQRF+ICjCDwRF+IGg+OnuFjB27Nhkfc6cOQ3b944dO5L1r7/+umH7Rrk48gNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIzzt4Bbb701WZ88eXLN2z548GCyvmzZsmT93nvvTdY7OjqS9Xrk9f7kk08m6/VMDx4BR34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCMrcvXk7M2vezk4gGzduTNanTJlS87Y3bdqUrB85ciRZnz79+AmaW0d3d3ey3t5eeZKonp6eottpGe5u1TyPIz8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBJU7zm9miyTdJqnX3Sdlyx6V9FtJX2RPe9jd/5a7M8b5B7V///5k/cILL2xSJ0O3bt26ZH358uUVaw899FBy3YkTJ9bUU78NGzZUrN100011bbuVFTnO/6Kkwa70+Iu7X5X9yw0+gNaSG3533yDpUBN6AdBE9bznf9DM3jOzRWY2srCOADRFreFfIOkySVdJOiDp6UpPNLNOM9tmZttq3BeABqgp/O7e4+7fufsxSc9Juibx3IXu3u7ulb9lAaDpagq/mbUNeHinpF3FtAOgWXJ/utvMlkq6UdL5ZtYlab6kG83sKkkuaZ+k+xrYI4AGyA2/u88eZPHzDegFJTh8+HCy/uabbybrnZ2dyXrqt/e3b9+eXPfdd99N1vNcfvnlda0/3HGFHxAU4QeCIvxAUIQfCIrwA0ERfiAopugObvPmzcn6zJkzG7bv8847r2HblvKnH4+OIz8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBMU4/zCwZcuWmtddsmRJgZ380KWXXlqxtnjx4rq2ffTo0WT9nXfeqWv7wx1HfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IinH+YWD06NEVazfccENy3a6urrr2fdpppyXr999/f8XamDFj6tr37t27k/VXXnmlru0Pdxz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAoc/f0E8zGSXpJ0gWSjkla6O7Pmtm5kpZLGi9pn6S73f3LnG2ldxbUggULkvW8abBT8sa6H3/88WT99NNPT9bnzp2brM+YMSNZT/nss8+S9enTpyfre/furXnfJzJ3t2qeV82R/6ik37v7jyX9TNIDZnalpLmS1rv7BEnrs8cAThC54Xf3A+6+I7t/WNIeSWMkzZDU/1MsiyXd0agmARRvSO/5zWy8pMmStkga7e4HpL4/EJJGFd0cgMap+tp+MztL0quS5rj7V2ZVva2QmXVKqv1NK4CGqOrIb2Yj1Bf8l919Zba4x8zasnqbpN7B1nX3he7e7u7tRTQMoBi54be+Q/zzkva4+zMDSqsldWT3OyS9Xnx7ABqlmqG+6yVtlPS++ob6JOlh9b3vXyHpIkmfS5rl7odytsVQ3yAmTpyYrK9duzZZv+CCC4psp2mOHTuWrM+aNStZX7VqVZHtDBvVDvXlvud3902SKm3slqE0BaB1cIUfEBThB4Ii/EBQhB8IivADQRF+IKjccf5Cd8Y4f02uvPLKZH3NmjUVa21tbcl1q71Mu1ap/19PPfVUct158+YV3U4IRX6lF8AwRPiBoAg/EBThB4Ii/EBQhB8IivADQTHOP8zl/ez3tddem6x/8803yfr+/fuT9e7u7oq1F154IbkuasM4P4Akwg8ERfiBoAg/EBThB4Ii/EBQhB8IinF+YJhhnB9AEuEHgiL8QFCEHwiK8ANBEX4gKMIPBJUbfjMbZ2b/MLM9ZvaBmf0uW/6omf3XzHZm/37Z+HYBFCX3Ih8za5PU5u47zOxsSdsl3SHpbklH3P3PVe+Mi3yAhqv2Ip9TqtjQAUkHsvuHzWyPpDH1tQegbEN6z29m4yVNlrQlW/Sgmb1nZovMbGSFdTrNbJuZbaurUwCFqvrafjM7S9I/JT3m7ivNbLSkg5Jc0h/V99bg1znb4LQfaLBqT/urCr+ZjZD0hqQ17v7MIPXxkt5w90k52yH8QIMV9sUe65vG9XlJewYGP/sgsN+dknYNtUkA5anm0/7rJW2U9L6kY9nihyXNlnSV+k7790m6L/twMLUtjvxAgxV62l8Uwg80Ht/nB5BE+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCCr3BzwLdlDSfwY8Pj9b1opatbdW7Uuit1oV2dvF1T6xqd/n/8HOzba5e3tpDSS0am+t2pdEb7UqqzdO+4GgCD8QVNnhX1jy/lNatbdW7Uuit1qV0lup7/kBlKfsIz+AkpQSfjObbmYfmtleM5tbRg+VmNk+M3s/m3m41CnGsmnQes1s14Bl55rZOjP7OLsddJq0knpriZmbEzNLl/ratdqM100/7TezkyV9JGmqpC5JWyXNdvfdTW2kAjPbJ6nd3UsfEzazn0s6Iuml/tmQzOxPkg65+xPZH86R7v6HFuntUQ1x5uYG9VZpZulfqcTXrsgZr4tQxpH/Gkl73f1Td/9W0jJJM0roo+W5+wZJh45bPEPS4uz+YvX952m6Cr21BHc/4O47svuHJfXPLF3qa5foqxRlhH+MpP0DHneptab8dklrzWy7mXWW3cwgRvfPjJTdjiq5n+PlztzcTMfNLN0yr10tM14XrYzwDzabSCsNOVzn7j+V9AtJD2Snt6jOAkmXqW8atwOSni6zmWxm6VclzXH3r8rsZaBB+irldSsj/F2Sxg14PFZSdwl9DMrdu7PbXkmvqe9tSivp6Z8kNbvtLbmf/3P3Hnf/zt2PSXpOJb522czSr0p62d1XZotLf+0G66us162M8G+VNMHMLjGzUyXdI2l1CX38gJmdmX0QIzM7U9I0td7sw6sldWT3OyS9XmIv39MqMzdXmllaJb92rTbjdSkX+WRDGX+VdLKkRe7+WNObGISZXaq+o73U943HJWX2ZmZLJd2ovm999UiaL2mVpBWSLpL0uaRZ7t70D94q9Hajhjhzc4N6qzSz9BaV+NoVOeN1If1whR8QE1f4AUERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6n/Q4xk/LleamgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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a3HdVznhdST98ww+IiW/4AUERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6v8Af7lPn9vA968AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QFOEHgiL8QFCEHwiK8ANB/T/RuiQugwG6xAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC7VJREFUeJzt3V2oHHcZx/HvY6w3tRct0hhiNVWKKL1o5VAExSZISxUh9aKlvYoonl60YMELS29yDiKI+HolRBpMQfsCbW0Q8YWSvghSmhaxL7EvlFhjQmKJ0HolbR8vzkSO6Tk7m92ZnT3n+X4g7O7s7MzDkN+Z2X1m5h+ZiaR63jN0AZKGYfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxX13lmuLCI8nVDqWWbGOPNNteePiOsi4sWIeCUi7phmWZJmKyY9tz8itgAvAdcAx4CngJsz84URn3HPL/VsFnv+q4BXMvPVzPwPcC+we4rlSZqhacK/Hfj7qtfHmmn/JyIWI+JwRByeYl2SOjbND35rHVq867A+M/cB+8DDfmmeTLPnPwZcsur1h4Dj05UjaVamCf9TwGURcWlEvA+4CTjYTVmS+jbxYX9mvhURtwG/A7YA+zPz+c4qk9SriVt9E63M7/xS72Zyko+kjcvwS0UZfqkowy8VZfilogy/VNRMr+eXzsWhQ4dGvv/YY4+NfH9paanDajYf9/xSUYZfKsrwS0UZfqkowy8VZfilomz1aTCzvKJU7+aeXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKss+vXrVdljuNXbt29bbsCtzzS0UZfqkowy8VZfilogy/VJThl4oy/FJRU/X5I+Io8CbwNvBWZi50UZQ2jrY+/s6dOyde9vLy8sSfVbsuTvLZlZmvd7AcSTPkYb9U1LThT+D3EfF0RCx2UZCk2Zj2sP8zmXk8Ii4G/hARf83Mx1fP0PxR8A+DNGem2vNn5vHm8RTwEHDVGvPsy8wFfwyU5svE4Y+I8yPigjPPgWuB57oqTFK/pjns3wo8FBFnlvPLzPxtJ1VJ6l3M8t7pEeGN2jeYPvv4jz766Mj3vV5/MpkZ48xnq08qyvBLRRl+qSjDLxVl+KWiDL9UlK2+4tpadX3eers5R0Qds9UnaSTDLxVl+KWiDL9UlOGXijL8UlGGXyrKIbo3uSH7+OBlufPMPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWWff5Pbu3dvr8tv6+O33Z5bw3HPLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFtfb5I2I/8CXgVGZe3ky7CLgP2AEcBW7MzH/1V6ZGGXVN/jRDaIN9/M1snD3/z4Hrzpp2B/BIZl4GPNK8lrSBtIY/Mx8HTp81eTdwoHl+ALi+47ok9WzS7/xbM/MEQPN4cXclSZqF3s/tj4hFYLHv9Ug6N5Pu+U9GxDaA5vHUejNm5r7MXMjMhQnXJakHk4b/ILCneb4HeLibciTNSmv4I+Ie4E/AxyPiWER8DfgucE1EvAxc07yWtIFEZs5uZRGzW9km0ue999v69N53f+PJzBhnPs/wk4oy/FJRhl8qyvBLRRl+qSjDLxVlq28DaGvlTXPZ7vLy8sSfnXej2pib+VJkW32SRjL8UlGGXyrK8EtFGX6pKMMvFWX4paLs88+BpaWlke/3Pcx2RZv5luT2+SWNZPilogy/VJThl4oy/FJRhl8qyvBLRfU+XJfazXMff8jr/a+++uqR709zH4O2z27kPv+43PNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGtff6I2A98CTiVmZc305aArwP/bGa7MzN/01eRG13b9fp9auvTt/Wz57nfPct7UWxG4+z5fw5ct8b0H2XmFc0/gy9tMK3hz8zHgdMzqEXSDE3znf+2iPhLROyPiAs7q0jSTEwa/p8CHwOuAE4AP1hvxohYjIjDEXF4wnVJ6sFE4c/Mk5n5dma+A/wMuGrEvPsycyEzFyYtUlL3Jgp/RGxb9fLLwHPdlCNpVsZp9d0D7AQ+EBHHgL3Azoi4AkjgKHBLjzVK6kFr+DPz5jUm39VDLZtW39frR4x1m/YNZ8jzIyrwDD+pKMMvFWX4paIMv1SU4ZeKMvxSUQ7RPQN9b+ON2upru332oUOHelv3Rt1m43CIbkkjGX6pKMMvFWX4paIMv1SU4ZeKMvxSUQ7Rram09epHvT/tpc5ttxXftWvXVMvf7NzzS0UZfqkowy8VZfilogy/VJThl4oy/FJR9vlnoK0f3dYrb9PnLa77vu34KPbx++WeXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKar1vf0RcAtwNfBB4B9iXmT+JiIuA+4AdwFHgxsz8V8uySt63v80sx06YJ219+rY+v9bW5X373wK+mZmfAD4N3BoRnwTuAB7JzMuAR5rXkjaI1vBn5onMfKZ5/iZwBNgO7AYONLMdAK7vq0hJ3Tun7/wRsQO4EngS2JqZJ2DlDwRwcdfFSerP2Of2R8T7gQeA2zPzjXHHOouIRWBxsvIk9WWsPX9EnMdK8H+RmQ82k09GxLbm/W3AqbU+m5n7MnMhMxe6KFhSN1rDHyu7+LuAI5n5w1VvHQT2NM/3AA93X56kvozT6vss8ATwLCutPoA7Wfnefz/wYeA14IbMPN2yrJo9rRZDDlXdZnl5earP93m5sdY2bquv9Tt/Zv4RWG9hnz+XoiTND8/wk4oy/FJRhl8qyvBLRRl+qSjDLxXV2ufvdGX2+aXedXlJr6RNyPBLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UZfqkowy8VZfilogy/VJThl4pqDX9EXBIRhyLiSEQ8HxHfaKYvRcQ/IuLPzb8v9l+upK60DtoREduAbZn5TERcADwNXA/cCPw7M78/9soctEPq3biDdrx3jAWdAE40z9+MiCPA9unKkzS0c/rOHxE7gCuBJ5tJt0XEXyJif0RcuM5nFiPicEQcnqpSSZ0ae6y+iHg/8Bjwncx8MCK2Aq8DCXybla8GX21Zhof9Us/GPewfK/wRcR7wa+B3mfnDNd7fAfw6My9vWY7hl3rW2UCdERHAXcCR1cFvfgg848vAc+dapKThjPNr/2eBJ4BngXeayXcCNwNXsHLYfxS4pflxcNSy3PNLPev0sL8rhl/qX2eH/ZI2J8MvFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UZfqkowy8VZfilogy/VJThl4oy/FJRrTfw7NjrwN9Wvf5AM20ezWtt81oXWNukuqztI+POONPr+d+18ojDmbkwWAEjzGtt81oXWNukhqrNw36pKMMvFTV0+PcNvP5R5rW2ea0LrG1Sg9Q26Hd+ScMZes8vaSCDhD8irouIFyPilYi4Y4ga1hMRRyPi2Wbk4UGHGGuGQTsVEc+tmnZRRPwhIl5uHtccJm2g2uZi5OYRI0sPuu3mbcTrmR/2R8QW4CXgGuAY8BRwc2a+MNNC1hERR4GFzBy8JxwRnwP+Ddx9ZjSkiPgecDozv9v84bwwM781J7UtcY4jN/dU23ojS3+FAbddlyNed2GIPf9VwCuZ+Wpm/ge4F9g9QB1zLzMfB06fNXk3cKB5foCV/zwzt05tcyEzT2TmM83zN4EzI0sPuu1G1DWIIcK/Hfj7qtfHmK8hvxP4fUQ8HRGLQxezhq1nRkZqHi8euJ6ztY7cPEtnjSw9N9tukhGvuzZE+NcaTWSeWg6fycxPAV8Abm0ObzWenwIfY2UYtxPAD4YsphlZ+gHg9sx8Y8haVlujrkG22xDhPwZcsur1h4DjA9Sxpsw83jyeAh5i5WvKPDl5ZpDU5vHUwPX8T2aezMy3M/Md4GcMuO2akaUfAH6RmQ82kwffdmvVNdR2GyL8TwGXRcSlEfE+4Cbg4AB1vEtEnN/8EENEnA9cy/yNPnwQ2NM83wM8PGAt/2deRm5eb2RpBt528zbi9SAn+TStjB8DW4D9mfmdmRexhoj4KCt7e1i54vGXQ9YWEfcAO1m56usksBf4FXA/8GHgNeCGzJz5D2/r1LaTcxy5uafa1htZ+kkG3HZdjnjdST2e4SfV5Bl+UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeK+i804L4sRqz26QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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e8g/ecnq7TuOcublJveXNLP2WKnzuypzxupR+OMMPiIkz/ICgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBPV/bIZWlxxAK+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i3vJmlt6tGj+7Mme8LqUfjvADYuIIPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0/UGV4xLbpyGUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EHwiK8ANBEX4gKMIPBEX4gaAIPxDUPwAV+Ty+b3QUzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yezOzTZJma+BXX72Slkt6VdIWSVMl/UnSAndv+hdvFXqbrWHO3Nyg3irNLN2tEt+7Ime8LqQfzvADYuIMPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0/M+tGGP6pgJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EHwiK8ANBEX4gKMIPBEX4gaAIPxDU/wF8GzMNJUwHZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V+NqVOeN1Kf3wCT8gJj7hBwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gqP8Hbm8sPkDW+/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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U/wPNJTux4/fMMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DQRF+ICjCDwRF+IGg/h9zJSYO0w0CWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for  i in range(100):\n",
    "    show_img(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = np.reshape(data, (head[1], head[2], head[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F+IGgCD8QFOEHgiL8QFCEHwiK8ANB/R/7QknxGq+fLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.imshow(imgs[i], cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'COL_NAMES': ['label', 'data'],\n",
       " 'target': array([0., 0., 0., ..., 9., 9., 9.]),\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original', data_home='./')\n",
    "mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * DESCR:数据集描述\n",
    "   * data: 一个数组， 每个实例为一行，每个特征为一列\n",
    "   * target: 一个带有标签的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = mnist['data'], mnist['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show_img1(i):\n",
    "    some_digit = X[i]\n",
    "    some_digit_image = some_digit.reshape(28,28)\n",
    "    plt.imshow(some_digit_image, cmap=matplotlib.cm.binary)\n",
    "    plt.show()\n",
    "    \n",
    "def show_img2(i):\n",
    "    some_digit = X[i]\n",
    "    some_digit_image = some_digit.reshape(28,28)\n",
    "    plt.imshow(some_digit_image, cmap=matplotlib.cm.binary, interpolation='nearest')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range(37001,37002):\n",
    "    show_img1(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_img2(37001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_digit = X[37001]\n",
    "show_img1(37001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[37000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建立测试集和训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[0:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14617, 40157, 21844, ...,  6376, 31680,  3973])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据集集合交叉洗牌， 交叉验证时， 每个子集合数据分布均匀， 有些机器学习算法对训练实例的顺序敏感\n",
    "import numpy as np\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "shuffle_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 6., 3., ..., 1., 5., 0.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练一个二元分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 识别数字5， 是5或非5\n",
    "y_train_6 = (y_train==6)\n",
    "y_train_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       ...,\n",
       "       [False,  True, False, ..., False, False, False],\n",
       "       [False, False, False, ...,  True, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_6.reshape(20,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_6 = (y_test==6)\n",
    "y_test_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_6.reshape(20,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SGD 梯度下降分类器， 适合非常大的数据集， 独立处理训练集数据， 一次一个， 适合在线学习\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_6)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 性能考核\n",
    "### 使用交叉验证测量精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.97708524, 0.97833514, 0.97441667, 0.96949746, 0.97116426])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估分类器比评估回归器更困难得多\n",
    "# 3个折叠，正确率达到95%以上\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_6, cv=5, scoring='accuracy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把每张图分类成非6\n",
    "\n",
    "from sklearn.base import BaseEstimator\n",
    "class Never6Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X),1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((len(X),1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89933333, 0.90116667, 0.90566667, 0.89975   , 0.90091667])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_6_clf = Never6Classifier()\n",
    "cross_val_score(never_6_clf, X_train, y_train_6, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 准确度超过90%， 里面只有10%是5， 猜一张照片不是5， 肯定有909%的几率是对的\n",
    "   * 这说明准确性无法成为分类器的首要性指标， 特别是当你处理偏科数据集， 某些类比其他类更为频繁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 混淆矩阵\n",
    "   * 评估分类器性能的更好方法是混淆矩阵\n",
    "   * A类别实例被分为B类别次数\n",
    "   * 想要知道分类器将数字3和数字5混淆多少次，通过混淆矩阵的5行3列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_6, cv=3)\n",
    "y_train_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 与cross_val_score相比\n",
    "   * 同样执行交叉验证\n",
    "   * 返回的不是评估分数，是每个折叠的预测\n",
    "   * 每一个实例在模型预测时使用的数据， 在训练期间从未遇见"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53296,   786],\n",
       "       [  442,  5476]], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_6, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 行表示实际类别， 列表示预测类别\n",
    "   * 第一行，第一列， 53296 被正确的分为非6， 真负类\n",
    "   * 第一行， 第二列， 786被错误的分为6， 假正类\n",
    "   * 第二行， 第一列， 442被错误的分为非6， 假负类\n",
    "   * 第二行， 第二列， 5476被正确的分为6， 真正类 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正类预测的准确率 被称为分类器的精度\n",
    "\n",
    "\n",
    "$\n",
    "\\text{精度} = \\cfrac{TP}{TP + FP}\n",
    "$\n",
    "\n",
    "TP是真正类的数量，FP是假正类的数量\n",
    "\n",
    "\n",
    "\n",
    "$\n",
    "\\text{召回率TPR} = \\cfrac{TP}{TP + FN}\n",
    "$\n",
    "* 检测正类实例的比例\n",
    "FN是假负类的数量\n",
    "![jupyter](./zhaohui.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 精度和召回率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8744809964867455"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_6, y_train_pred) # 5476/(5476+786)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8744809964867455"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5476/(5476+786)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9253126056100034"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_6, y_train_pred) # 5394/(5394+524)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9253126056100034"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5476/(5476+442)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 说明检测一张图的时候， 只有90%的概率是准确的， 而且只有90.2%的数字6被它检测出来了\n",
    "   * 精度和召回率合成单一指标， 成为F1分数， 谐波平均值\n",
    "   * 平均值平等对待所有的值， 谐波平均值会给与较低值更高的权重， 只有召回率和精度都很高时， 才能获得较高的F1分数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\n",
    "F_1 = \\cfrac{2}{\\cfrac{1}{\\text{precision}} + \\cfrac{1}{\\text{recall}}} = 2 \\times \\cfrac{\\text{precision}\\, \\times \\, \\text{recall}}{\\text{precision}\\, + \\, \\text{recall}} = \\cfrac{TP}{TP + \\cfrac{FN + FP}{2}}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8991789819376027"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_train_6, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * F1分数对那些具有相近精度和召回率的分类器更有利， 不一定符合你的期望\n",
    "   * 有时候你关心精度， 有时候你关心召回率\n",
    "   * 训练一个分类器检测儿童可以放心观看的视频， 你可能要求阻拦了很多很好的视频， 低召回率， 保留下来都是安全的视频，高精度\n",
    "   * 不能同时增加精度和召回率，反之亦然"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 精度/召回率权衡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![jupyter](./quanheng.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * SGDClassifier对每个实例基于决策函数计算一个分值， 大于阈值为正类， 反之为负类\n",
    "   * 中间阈值右侧找到4个真正类， 一个假正类， 精度为4/5， 80%\n",
    "   * 在6个真正的5内， 分类器只找到4个，所以召回率是4/6, 67%\n",
    "   * 提高阈值， 向右移动， 精度提高， 召回率降低\n",
    "   * 反之阈值降低，召回率提高，精度降低\n",
    "   * sklearn不能直接设置阈值， 可以访问决策分数\n",
    "   * SGDClassifier默认阈值为零"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如何设置阀值\n",
    "\n",
    "# 用predict_proba得到每个实例属于正类的概率，然后对概率切一下。以LogisticRegression为例\n",
    "# clf = LogisticRegression()\n",
    "# clf.fit(X_train, y_train)\n",
    "# pred_proba = clf.predict_proba(X_test)[:, 1]\n",
    "# threshold = 0.75  # 阀值设置为0.75\n",
    "# pred_label = pred_proba > threshold\n",
    "\n",
    "# pred_proba是每个实例为真的概率\n",
    "# 假设阈值是0.75\n",
    "# pred_label里True就是概率大于0.75的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([307136.23236343])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回决策值， decision_function\n",
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores>threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提高阈值， 降低召回率， 提高阈值到20000， 就错了这个图\n",
    "threshold = 2000000\n",
    "y_some_digit_pred = (y_scores>threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -278456.7373371 ,   220800.83754026, -1378647.61345845, ...,\n",
       "        -485638.13215383,  -648474.35063981,  -315346.47724354])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如何确定使用什么阈值\n",
    "# 返回决策分数而不是预测结果\n",
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_6, cv=5, method='decision_function')\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_6, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用matplotlib 绘制精度和召回相对于阀值的函数图\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-800000, 800000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"召回\", fontsize=16)\n",
    "    plt.ylabel(\"精度\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过选择阈值来实现最佳精度/召回率权衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#目标设置为90%精度， 阈值大约是10000左右\n",
    "y_train_pred_90 = (y_scores>10000)\n",
    "y_train_pred_90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9346510689794272"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_6, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7830348090571139"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_6, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "   * 获得了一个90%精度的分类器， 但召回太低， 精度再高也不怎么有用\n",
    "   * 如果工作中要求的精度是99%， 你应该考虑召回率是多少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC曲线\n",
    "   * 本质是真正类率TPR和假正类率FPR（被错误分类成正类的负类实例比列）\n",
    "   * 与召回/精度曲线非常相似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_6, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('假正类率', fontsize=16)\n",
    "    plt.ylabel('真正类率', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9871273852871257"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算曲面下面积AUC，虚线是随机分类， 0.5~1\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_train_6, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 召回率，TPR越高， 分类器的假分类FPR就越多\n",
    "   * 虚线表示纯随机分类器的ROC曲线， 好的分类器应该远离这条曲线，往左上角\n",
    "   * 是使用精度/召回率 PR曲线还是ROC曲线， 关键在于正类非常少或者更关注假正类而不是假负类， 选择PR反之选择ROC\n",
    "   * 例如： 前面例子ROC曲线很不错是因为跟负类非6相比， 正类数据6真的很少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练随机森林分类器， 比较SGD分类器的ROC曲线和ROC AUC分数\n",
    "   * 获取训练集中每个实例的分数\n",
    "   * RandomForestClassifier没有decision_function方法但是有predict_proba方法，sklearn的分类器都有这两个方法的一个\n",
    "   * predict_proba返回一个矩阵， 每行一个实例， 每列代表一个类别的概率， 比如这个图片， 70%是5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.9, 0.1],\n",
       "       [0.2, 0.8],\n",
       "       [1. , 0. ],\n",
       "       ...,\n",
       "       [1. , 0. ],\n",
       "       [1. , 0. ],\n",
       "       [1. , 0. ]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_6, cv=5, method='predict_proba')\n",
    "y_probas_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1, 0.8, 0. , ..., 0. , 0. , 0. ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制ROC曲线，需要的是决策值而不是概率， 直接使用正类的概率作为决策值\n",
    "y_scores_forest = y_probas_forest[:,1]\n",
    "y_scores_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_forest, tpr_forest, threshold_forest = roc_curve(y_train_6, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 3.69808809e-05, 7.39617618e-05, 1.84904404e-04,\n",
       "        6.47165415e-04, 1.33131171e-03, 2.73658519e-03, 5.14034244e-03,\n",
       "        1.03546466e-02, 2.74398136e-02, 9.72227358e-02, 1.00000000e+00]),\n",
       " array([0.        , 0.53937141, 0.73436972, 0.82342007, 0.87850625,\n",
       "        0.91770869, 0.94423792, 0.96468401, 0.97854005, 0.98884758,\n",
       "        0.9950997 , 1.        ]),\n",
       " array([2. , 1. , 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0. ]))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr_forest, tpr_forest, threshold_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(fpr, tpr, 'b:', linewidth=2, label='SGD')\n",
    "plot_roc_curve(fpr_forest, tpr_forest, 'random_forest')\n",
    "plt.legend(loc='lower right', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9963913021618043"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomForestClassifier比SGD效果好很多， ROC AUC的分数也高很多\n",
    "roc_auc_score(y_train_6, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 再看一下精度和召回率和很高\n",
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_6, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.986916227512266"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_6, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177086853666779"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_6, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "   * 选择合适的指标利用交叉验证来对分类器进行评估\n",
    "   * 选择满足需求的精度/召回率权衡\n",
    "   * 使用ROC曲线和ROC AUC分数比较多个模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多类别分类器\n",
    "   * 尝试6之外的检测\n",
    "   * 多类别分类器，区分两个以上的类别\n",
    "   * 随机森林和朴素贝叶斯可以直接处理多个类别\n",
    "   * 支持向量机SVM，线性分类器， 只可以处理2元分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 解决方案\n",
    "   1. 将数字分类0-9， 训练10个二元分类器， 每个数字一个， 检测一张图片时， 获取每个分类器的决策分数，哪个最高属于哪个， 称为一对多， OVA\n",
    "   2. 为每一对数字训练一个二元分类器， 比如区分0-1， 0-2,0-3，。。。每一对为一个OVO策略， 存在N个类别， 需要K=N*(N-1)/2个分类器， 最后看哪个类别获胜最多。K也相当于Ncombination2, K=10C2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 优缺点\n",
    "   * OVO只需要用到部分训练集对其必须区分两个类别进行训练\n",
    "   * 对于较小训练哪集合，OVO比较有优势， 大训练集合就OVA速度快， 所以OVA更常用，比如SVM在数据规模扩大时表现糟糕\n",
    "   * sklearn检查到使用二元分类算法进行多类别分类任务会自动运行OVA， SVM分类器除外"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([6.])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-791462.66343123, -563032.41214774, -401817.73289352,\n",
       "        -549748.51475724, -205120.36608642, -354193.92068065,\n",
       "         307136.23236343, -703336.26509775, -569717.58740033,\n",
       "        -355592.7522028 ]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 内部实际上训练了10个二元分类器， 获得图片的决策分数， 然后选择了分数最高的类别， 返回10个分数，每个类别一个\n",
    "some_digit_scores = sgd_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_class = np.argmax(some_digit_scores)\n",
    "num_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 目标类别列表表会存储在classes_这个属性中， 按值大小排列\n",
    "sgd_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = sgd_clf.classes_[num_class]\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用OVO策略， 一对一或者一对多\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, tol=-np.infty, random_state=42))\n",
    "ovo_clf.fit(X_train, y_train)\n",
    "ovo_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovo_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用随机森林\n",
    "forest_clf.fit(X_train, y_train)\n",
    "forest_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林直接将实例分为多个类别， 调用predict_proba()可以获得分类器将每个实例分类成每个类别的概率表\n",
    "forest_clf.predict_proba([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.86588921, 0.88051991, 0.85683333, 0.87646912, 0.8496999 ])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用交叉验证评估SGD的准确率\n",
    "cross_val_score(sgd_clf, X_train, y_train, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.90845481, 0.91043159, 0.912     , 0.91622906, 0.90746916])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将输入进行简答缩放， 可以提高准确度到90%以上\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_trained_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_trained_scaled, y_train, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 错误分析\n",
    "### 项目流程\n",
    "   1. 探索数据准备的选项\n",
    "   2. 尝试多个模型\n",
    "   3. 选择最佳模型并用GridSearchCV对参数进行微调\n",
    "   4. 尽可能自动化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 确定了一个相对合适的模型， 进一步优化，分析其错误类型\n",
    "  * 查看混淆矩阵\n",
    "  * 使用cross_val_predict()进行预测\n",
    "  * 调用confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\software\\python37\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5735,    2,   20,   11,   10,   49,   44,    8,   39,    5],\n",
       "       [   2, 6463,   47,   31,    6,   39,    6,   13,  124,   11],\n",
       "       [  58,   31, 5349,   95,   83,   25,   91,   57,  155,   14],\n",
       "       [  49,   38,  130, 5385,    3,  210,   33,   54,  136,   93],\n",
       "       [  18,   23,   37,    9, 5380,   10,   50,   38,   75,  202],\n",
       "       [  70,   38,   34,  202,   69, 4602,  100,   34,  170,  102],\n",
       "       [  35,   26,   54,    2,   41,   93, 5611,    5,   51,    0],\n",
       "       [  23,   18,   68,   29,   59,   10,    6, 5825,   15,  212],\n",
       "       [  51,  150,   79,  158,   15,  149,   58,   31, 5027,  133],\n",
       "       [  43,   28,   25,   90,  162,   30,    2,  215,   76, 5278]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_trained_scaled, y_train, cv=5)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用matplotlib的matshow函数来查看混淆矩阵的图像表示\n",
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.68259328e-01, 3.37666723e-04, 3.37666723e-03, 1.85716698e-03,\n",
       "        1.68833361e-03, 8.27283471e-03, 7.42866790e-03, 1.35066689e-03,\n",
       "        6.58450110e-03, 8.44166807e-04],\n",
       "       [2.96647879e-04, 9.58617621e-01, 6.97122516e-03, 4.59804212e-03,\n",
       "        8.89943637e-04, 5.78463364e-03, 8.89943637e-04, 1.92821121e-03,\n",
       "        1.83921685e-02, 1.63156333e-03],\n",
       "       [9.73481034e-03, 5.20308828e-03, 8.97784491e-01, 1.59449480e-02,\n",
       "        1.39308493e-02, 4.19603894e-03, 1.52735817e-02, 9.56696878e-03,\n",
       "        2.60154414e-02, 2.34978181e-03],\n",
       "       [7.99217093e-03, 6.19801011e-03, 2.12037188e-02, 8.78323275e-01,\n",
       "        4.89316588e-04, 3.42521611e-02, 5.38248247e-03, 8.80769858e-03,\n",
       "        2.21823520e-02, 1.51688142e-02],\n",
       "       [3.08113660e-03, 3.93700787e-03, 6.33344745e-03, 1.54056830e-03,\n",
       "        9.20917494e-01, 1.71174255e-03, 8.55871277e-03, 6.50462170e-03,\n",
       "        1.28380692e-02, 3.45771996e-02],\n",
       "       [1.29127467e-02, 7.00977679e-03, 6.27190555e-03, 3.72624977e-02,\n",
       "        1.27282789e-02, 8.48920863e-01, 1.84467810e-02, 6.27190555e-03,\n",
       "        3.13595278e-02, 1.88157167e-02],\n",
       "       [5.91416019e-03, 4.39337614e-03, 9.12470429e-03, 3.37952011e-04,\n",
       "        6.92801622e-03, 1.57147685e-02, 9.48124366e-01, 8.44880027e-04,\n",
       "        8.61777628e-03, 0.00000000e+00],\n",
       "       [3.67118915e-03, 2.87310455e-03, 1.08539505e-02, 4.62889066e-03,\n",
       "        9.41739824e-03, 1.59616919e-03, 9.57701516e-04, 9.29768555e-01,\n",
       "        2.39425379e-03, 3.38387869e-02],\n",
       "       [8.71645873e-03, 2.56366433e-02, 1.35019655e-02, 2.70039310e-02,\n",
       "        2.56366433e-03, 2.54657324e-02, 9.91283541e-03, 5.29823962e-03,\n",
       "        8.59169373e-01, 2.27311571e-02],\n",
       "       [7.22810556e-03, 4.70667339e-03, 4.20238696e-03, 1.51285930e-02,\n",
       "        2.72314675e-02, 5.04286435e-03, 3.36190956e-04, 3.61405278e-02,\n",
       "        1.27752563e-02, 8.87207934e-01]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看起来不错， 大多数白色图片都在主对角线上， 说明他们被正确分类\n",
    "# 数字5看起来比较暗， 说明1.数字5的图片较少， 2. 分类器在数字5执行效果不如其他数字好\n",
    "# 假设把焦点放到错误上为取得错误率， 而不是错误绝对值， 需要将混淆矩阵每一个值除以相应类别中的图片数量\n",
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx/row_sums\n",
    "norm_conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAEACAYAAAB4TnCPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC59JREFUeJzt3X+I1/d9wPHnS70T41y9ZFlkIAmGkT+mpB2HnZCOxDjpCKUsOCqUJjQbpqPY/BVI2Yqsf2xQkkJoNPT6I//Yba6RDRo66AhIa2gYjiZ/xCoImpQmR7Mm6nrmh3e+9od3JCSr38/J530fj9fz8depH1/3Qn36/t75uY+RmUiqY8XQC0haWkYvFWP0UjFGLxVj9FIxRi8VY/QdRMRHIuI/IuJHEfFvETE+9E5dRMRNEfGzofdYjIg4EBGfGnqPLiJiIiJ+GBHHIuKbQ+/T1SDRR8R3IuKnEfF3Q7z/q/BZ4OuZuROYBj458D5dPQqsGXqJriLiE8CGzPzB0Lt09Dnge5k5CayLiMmhF+piyaOPiHuBlZm5DdgUEX+41DssVmYeyMz/nP/mjcCvhtyni4jYDsxw+S+pa15EjAHfAs5ExKeH3qejXwObI2I9sBH4xcD7dDLESX8n8K/zb/8IuGOAHa5KRGwDJjLz+aF3uZL5Dz++Ajwy9C6LcB9wHPgasDUi9g68TxdHgZuBLwE/B94Ydp1uhoh+LfDL+bffAG4aYIdFi4jrgW8ADwy9SwePAAcy8+zQiyzCx4CpzJwGDgJ3DbxPF/uAL2TmV4ETwOcH3qeTIaL/De99nPk7A+2wKPMn5/eBL2fmy0Pv08EO4IsRcQT4aER8e+B9ujgFbJp/exJYDr/OE8CWiFgJfBxYFl/IEkv9BTcRcR/w+5n5aET8PXAyM/9pSZdYpIj4G+AfgBfnv+vJzDw04EqdRcSRzLxz6D1GiYh1wHe5/MpvDNiVmb+88s8aVkRsBZ7i8kv8nwJ/kZm/GXar0YaI/neBnwDPAn8O/ElmnlvSJaTCljx6uPzvm8CfAT+e/xhO0hIZJHpJw7nmP4kmqV9GLxUzWPQRsWeo93213Lm95bYvLL+dhzzpl9Uv1Dx3bm+57QvLbGdf3kvF9PrZ+4hYdv8UsGrVqs7XXrp0iRUruv09eenSpatdqVeZSUR0unbNmjZfkLeYP2Ozs7OL+j156623rmalkVavXt352rm5OVauXNn5+nfeeedqVrqizCQzO/1Gd//VHVDX0K7GDTfc0GTuzMxMk7mwuIgWY8uWLU3mXrx4sclcgOPHjzeZe+uttzaZC3Dy5MneZ87Ozna+1pf3UjFGLxVj9FIxRi8VY/RSMUYvFdMp+mX49FpJv8XI6Jfj02sl/XZdTvo7WaZPr5X0YV2iv+LTayNiz/z/8HGs7+Uk9a/LbbhXfHptZk4BU7A8772Xquly0v83772kvx0402wbSc11Oen/HfhJRPwB80+vbbuSpJZGnvSZeZ7Ln8x7HrjLx1VLy1unL63NzDd57zP4kpYx78iTijF6qRijl4oxeqmY3p+R1+J5di0fMrlhw4Ymc8+ebfdfw7/22mtN5r755ptN5p4+fbrJXFjcAywX4957720yF+Cxxx7rfebc3Fznaz3ppWKMXirG6KVijF4qxuilYoxeKsbopWKMXirG6KVijF4qxuilYoxeKsbopWKMXirG6KVijF4qxuilYoxeKsbopWKMXirG6KVijF4qptdHYK9bt47Jyck+RwLwyiuv9D5zwYsvvthk7t69e5vMBTh//nyTuc8++2yTuQ888ECTuQAnT55sMrfFn+MFY2Njvc98++23O1/rSS8VY/RSMUYvFWP0UjFGLxVj9FIxRi8VY/RSMSNvzomIjwD/AqwEZoDPZOa7rReT1EaXk/6zwNczcycwDXyy7UqSWhp50mfmgfd980bgV+3WkdRa53vvI2IbMJGZz3/g+/cAewBWr17d73aSetfpE3kRcT3wDeBDXzmRmVOZOZmZk+Pj433vJ6lnI6OPiHHg+8CXM/Pl9itJaqnLSf9XwB8DfxsRRyLiM413ktRQl0/kPQk8uQS7SFoC3pwjFWP0UjFGLxVj9FIxRi8V0+vTcOfm5piZmelzJND2Tr/HH3+8ydyHHnqoyVyAlStXNpk7PT3dZO62bduazAXYsGFDk7mvvvpqk7kAu3fv7n3m4cOHO1/rSS8VY/RSMUYvFWP0UjFGLxVj9FIxRi8VY/RSMUYvFWP0UjFGLxVj9FIxRi8VY/RSMUYvFWP0UjFGLxVj9FIxRi8VY/RSMUYvFWP0UjGRmb0NGxsby4mJid7mLWj5/96vX7++ydzTp083mQtw4cKFJnPXrl3bZO7tt9/eZC7A+fPnm8zdvHlzk7kA+/fv733m9u3beeGFF6LLtZ70UjFGLxVj9FIxRi8VY/RSMUYvFWP0UjGdoo+ImyLiZ62XkdRe15P+UWBNy0UkLY2R0UfEdmAGmG6/jqTWrhh9RIwDXwEeWZp1JLW2asSPPwIcyMyzEf//bb0RsQfYA7BihZ8XlK51oyrdAXwxIo4AH42Ib3/wgsycyszJzJw0eunad8WTPjP/dOHtiDiSmX/dfiVJLXU+mjPzzoZ7SFoivh6XijF6qRijl4oxeqkYo5eKMXqpmFF35C3KunXruPvuu/scCcCJEyd6n7ng4sWLTebOzMw0mQuwe/fuJnMPHTrUZO6OHTuazAU4fPhwk7n79u1rMhfgiSee6H3m66+/3vlaT3qpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qZjIzN6GrVmzJm+55Zbe5i24cOFC7zMXtNgX4OjRo03mAmzZsqXJ3DvuuKPJ3P379zeZCzA2NtZk7tatW5vMBXjuueeazM3M6HKdJ71UjNFLxRi9VIzRS8UYvVSM0UvFGL1UjNFLxXSOPiIORMSnWi4jqb1O0UfEJ4ANmfmDxvtIamxk9BExBnwLOBMRn26/kqSWupz09wHHga8BWyNi7/t/MCL2RMSxiDg2OzvbYkdJPeoS/ceAqcycBg4Cd73/BzNzKjMnM3Ny1apVLXaU1KMu0Z8CNs2/PQm83G4dSa11OZq/A3w3InYDY8CutitJamlk9Jn5v8BfLsEukpaAN+dIxRi9VIzRS8UYvVSM0UvFGL1UTK+30M3NzXHu3Lk+RwLw7rvv9j5zwdq1a5vM3bhxY5O5ABMTE03mPvXUU03mjo+PN5kL7f5svPTSS03mAkxPT/c+c+fOnZ2v9aSXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4rp9Wm44+PjbNq0afSFi/Twww/3PnPB008/3WTu1NRUk7kAu3fvbjL34MGDTeYeOHCgyVyAM2fONJl76tSpJnMBnnnmmd5nLuYp1J70UjFGLxVj9FIxRi8VY/RSMUYvFWP0UjFXjD4iJiLihxFxLCK+uVRLSWpn1En/OeB7mTkJrIuIySXYSVJDo6L/NbA5ItYDG4FftF9JUkujoj8K3Ax8Cfg58EbzjSQ1NSr6fcAXMvOrwAng8x+8ICL2zH/Mf2x2drbFjpJ6NCr6CWBLRKwEPg7kBy/IzKnMnMzMyVWrev36HUkNjIr+H4Ep4BxwPfDPzTeS1NQVj+bM/C/gj5ZoF0lLwJtzpGKMXirG6KVijF4qxuilYoxeKsbopWIi80M32V216667Lm+77bbe5i04e/Zs7zMXtLp1+J577mkyF2DXrl1N5j744INN5rZ6TDXAhQsXmsy9//77m8wFOHToUJO5mRldrvOkl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eKMXqpGKOXijF6qRijl4oxeqkYo5eK6fVpuBHxOvByx8t/D/if3t750nDn9pbbvnBt7HxzZt7Y5cJeo1+MiDiWmZODvPOr5M7tLbd9Yfnt7Mt7qRijl4oZMvqpAd/31XLn9pbbvrDMdh7sY3pJw/DlvVSM0UvFGL1UjNFLxRi9VMz/Ae4oZlpn6KRsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用0填充对角线， 只保留错误值， 重新绘制\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 每行代表实例类别，每列代表预测类别\n",
    "   * 8和9列比较亮， 说明很多图片被错误地分类为8和9\n",
    "   * 8和9行也偏亮， 说明8和9这两个数字图片容易和其他数字图片混淆\n",
    "   * 有些行偏暗，比如1， 大多数数字都可以被正确分类，只有一些和8混淆\n",
    "   * 5和3是错误最多的， 比如看行3和列5是很白的，说明实际是3可是被误判成5， 看行5和列3页一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论\n",
    "  * 改进8和9的分类器\n",
    "  * 修正数字3和5的混淆"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何优化分类器\n",
    "   * 尝试多收集这些数字的训练集\n",
    "   * 开发一些新特征来改善分类器\n",
    "   * 优化分类器算法\n",
    "   * 使用pillow或opencv对图像预处理， 让现实模型更突出\n",
    "   * 分析单个错误"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看数字3和数字5的例子\n",
    "n1, n2 = 3,5\n",
    "X_an1_pn1 = X_train[(y_train == n1) & (y_train_pred == n1)]\n",
    "X_an1_pn2 = X_train[(y_train == n1) & (y_train_pred == n2)]\n",
    "X_an2_pn1 = X_train[(y_train == n2) & (y_train_pred == n1)]\n",
    "X_an2_pn2 = X_train[(y_train == n2) & (y_train_pred == n2)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(2,2,1)\n",
    "plot_digits(X_an1_pn1[:25], images_per_row=5)\n",
    "plt.subplot(2,2,2)\n",
    "plot_digits(X_an1_pn2[:25], images_per_row=5)\n",
    "plt.subplot(2,2,3)\n",
    "plot_digits(X_an2_pn1[:25], images_per_row=5)\n",
    "plt.subplot(2,2,4)\n",
    "plot_digits(X_an2_pn2[:25], images_per_row=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 左侧两个是被分类为3的图片\n",
    "   * 右侧两个是被分类为5的图片\n",
    "   * 大多数错误分类的图片看起来还是比较明显的错误\n",
    "   * 原因：SGD是一个线性模型， 它所做的就是为每个像素分配一个各个类别的权重， 当它看到新的图像，将加权后的像素强度汇总， 从而得到一个分数进行分类\n",
    "   * 数字3和5在一部分像素位上有区别，所以分类器很容易将其弄混\n",
    "   * 通过上面图像， 如果书写3的连接点左移， 分类器可能将其分类为数字5， 这个分类器对图像位置和旋转敏感\n",
    "   * 减少混淆的方法之一， 就是对图像进行预处理， 确保位于中心位置并没有旋转"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多标签分类\n",
    "   * 为每个实例产生多个类别， 列如一个照片识别多个脸\n",
    "   * 分类器经过训练可以识别小红， 小白， 小军， 一张照片里如果只有小红和小白，经过分类器的结果应该是[1,1,0]\n",
    "   * 输出多个二元标签的分类系统称为多标签分类系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train>=7)\n",
    "y_train_odd = (y_train %2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN支持多标签分类， 不是所有的分类器都支持\n",
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估多标签分类器的方法有很多， 方法之一就是测量每个标签的F1分数， 或者其他二元分类器指标， 然后简单平均\n",
    "# y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=5)\n",
    "# y_train_knn_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f1_score(y_multilabel, y_train_knn_pred, average='macro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上面假设所有标签都同等重要， 也可以给每个标签设置一个权重（该目标标签实例的数量），设置average='weighted' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多输出分类\n",
    "### 例子： 构建一个系统去除图片中噪声， 输入一张有噪声的图片， 它将输出一张干净的数字图片， 分类器输出是多个标签， 一个像素一个标签， 每个标签多个值， 0到255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加噪声， 目标将图片还原成原始图片， 创建训练集和测试集\n",
    "train_noise = np.random.randint(0,100, (len(X_train), 784))\n",
    "X_train_mod = X_train + train_noise\n",
    "test_noise = np.random.randint(0,100, (len(X_test), 784))\n",
    "X_test_mod = X_test + test_noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d277ffa888>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 5501\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(X_test_mod[some_index].reshape(28,28), cmap=matplotlib.cm.binary)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(y_test_mod[some_index].reshape(28,28), cmap=matplotlib.cm.binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(clean_digit.reshape(28,28), cmap=matplotlib.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
